The cross-talk between Trichoderma gamsii and Fusarium graminearum at distance:

The cross-talk between Trichoderma gamsii and Fusarium graminearum at distance:

a genome-wide transcriptome analysis

Candidate: Supervisor:

Antonio Zapparata Prof. Giovanni Vannacci Opponent:

Prof. Quirico Migheli Academic year: 2016/2017

A me stesso (3)

A chi persevera e, alla fine, vince!

Abstract

We investigated the cross-talk at distance between the promising biocontrol fungus Trichoderma gamsii T6085 and the mycotoxigenic fungal pathogen Fusarium graminearum ITEM 124, causal agent of Fusarium Head Blight, both at the physiological and the molecular levels. To this end, a genome-wide transcriptome analysis in both fungi during the sensing phase (before contact) was performed. The analysis of differentially expressed genes (DEGs) revealed two opposite behaviours: while an overall up-regulation occurred in F. graminearum ITEM 124 (‘buzzing mode’), gene patterns of T. gamsii T6085 were mainly down-regulated (‘stealth mode’). The functional analysis of DEGs revealed that T. gamsii T6085 successfully competed for iron by up-regulating a ferric reductase (one of the most strongly upregulated genes) involved in iron uptake, whereas F. graminearum ITEM 124 down-regulated the expression of seven stress-related enzymes having iron as cofactor. Furthermore, while F.

graminearum ITEM 124 up-regulated the entire repertoire of four killer toxin-like chitinases, T. gamsii T6085 decreased the level of interaction at distance by down-regulating six genes coding for secreted chitinases (‘stealth mode’). Finally, F. graminearum ITEM 124 grew faster in presence of T. gamsii T6085, suggesting an increased metabolic activity (‘buzzing mode’).

This study shows how the communication between Trichoderma and Fusarium is already regulated before contact. The study of gene expression profiles unravels a strongly different behaviour of the two fungi when they face each other. This opens new fields of investigation towards the successful development of T. gamsii T6085 as biocontrol agent.

Index 1.0 Introduction (p. 7)

2.0 Materials and methods (17) 2.1 Genome sequencing projects (17) 2.1.1 Tgam v1.0 (17)

2.1.2 Tgam v2.0 (17) 2.1.3 Fgra v1.0 (18)

2.2 Comparative genomic analyses (19)

2.2.1 Genome sampling in the Hypocreales order (19) 2.2.2 Identification of expanded/contracted InterPro IDs (19) 2.3 Biological assays of the sensing phase (19)

2.3.1 Experimental set up (19) 2.3.2 pH measurement (21)

2.3.3 Growth rate measurement in dual plate confrontation assay (21)

2.4 Genome-wide transcriptomic analysis of the sensing phase between Tgam and Fgra (21) 2.4.1 Pilot experiment (21)

2.4.2 RNA extraction and evaluation (22)

2.4.3 Library preparation and RNA sequencing (22)

2.4.4 Identification and annotation of differentially expressed genes (DEGs) (22) 2.4.5 Analysis of secreted DEGs (24)

2.5 Functional interpretation of DEGs (25)

2.5.1 Analysis of expanded/contracted InterPro IDs involved in the sensing phase (25) 2.5.2 GO term enrichment analysis on DEGs (25)

2.5.3 GO term enrichment analysis on secreted DEGs (25) 3.0 Results (26)

3.1 Genome sequencing projects (26) 3.1.1 Tgam v1.0 (26)

3.1.2 Tgam v2.0 (26) 3.1.3 Fgra v1.0 (26)

3.2 Comparative genomic analyses (27)

3.2.1 Genome sampling in the Hypocreales order (27) 3.2.2 Identification of expanded/contracted InterPro IDs (31)

3.3.2 pH measurement (47)

3.3.3 Growth rate measurement in dual plate confrontation assay (48)

3.4 Genome-wide transcriptomic analysis of the sensing phase between Tgam and Fgra (49) 3.4.1 Pilot experiment (49)

3.4.2 RNA extraction and evaluation (53)

3.4.3 Library preparation and RNA sequencing (55)

3.4.4 Identification and annotation of differentially expressed genes (DEGs) (57) 3.4.5 Analysis of secreted DEGs (62)

3.5 Functional interpretation of DEGs (63)

3.5.1 Analysis of expanded/contracted InterPro IDs involved in the sensing phase (63) 3.5.2 GO term enrichment analysis on DEGs (65)

3.5.3 GO term enrichment analysis on secreted DEGs (75) 4.0 Discussion (80)

5.0 Conclusions (88) Bibliography (91)

Abbreviations (throughout the thesis)

Tgam = Trichoderma gamsii T6085; Fgra = Fusarium graminearum ITEM 124

1.0 Introduction

No fungus is an island

Any organism has to communicate with the surrounding environment while carrying out its lifecycle. Let’s try to better define the concept of biocommunication. Guenther Witzany says that <<To communicate it is necessary that organisms have assets that serve as signs, signals or symbols, such as chemical molecules, either produced directly by the organism, or as secondary metabolites, or even molecules in the surroundings>> (Nickerson et al., 2012).

Each ecosystem is composed by a myriad of signalling molecules interacting each other and with organisms. It is worth noting that these signals do not necessarily have meaning as such, but it varies according to contexts. This also means that the same signs may transport different messages according to sender and receiver. Therefore, signs produced by a given species may have different meanings depending on the communicative organism(s). In fact, Witzany follows <<[..] no natural language can speak itself, these signs must be sensed and interpreted in the correct way by biological agents, i.e. there must be subjects of sign production and sign interpretation.>> (Nickerson et al., 2012)

The biocommunication determines the outcome of the relationship between two or more organisms sharing the same ecological niche. For instance, genetically distant organisms establishing a symbiotic relation achieve increased fitness they cannot achieve alone. This particular relationship is a “positive” symbiosis, referred to as mutualism. On the contrary, parasitism is beneficial to one whereas it is damaging to the other (Hirsch, 2004).

Fungi very frequently interact each other as they can be found in almost all the terrestrial ecosystems. Fungal hyphae of different individuals come into contact commonly, and the outcome of these interactions is a result of a never-ending evolution. When hyphae belong to the same organism they may fuse and can give rise to a heterokaryotic mycelium (this happens, for example, when genetically different individuals belong to the same anastomosis group).

Otherwise, they fail in this fusion process due to some incompatibilities (e.g., heterokaryon incompatibility). Hyphal Interference (HI), that is the death of hyphae from one species due to the meeting with another interacting species, is an example of a failing interaction (Ikediugwu and Webster, 1970). Sometimes fungi encounter each other as a result of a fine-tuned chemotropism. Mycoparasitism, in which the chemotropic phase represents the very first step of the interaction, may be interpreted as a more efficient form of mycelium invasion and

context, mycoparasitism has been characterized as the ancestral lifestyle of the Trichoderma genus (Kubicek et al., 2011). Trichoderma is a well-known model fungal genus for both fungal- fungal and plant-fungal interactions. Here we report, for the first time to our knowledge, about the cross-talk between two filamentous fungi, i.e. Trichoderma gamsii T6085 (Tgam) and Fusarium graminearum ITEM 124 (Fgra), at distance (sensing phase). In particular, we focused our attention on the functional characterization of the differentially expressed genes identified at the sensing phase of the non-self-interaction (Tgam vs. Fgra). Therefore, we did not investigate gene patterns over time, as most of the dedicated scientific literature has already done (some examples will be provided in this section), but rather studied the gene patterns characterizing the sensing phase of the non-self-interaction (compared to the self-interactions).

We contributed to deepen the knowledge about the biocommunication at distance in fungi functional for the development of Tgam as promising biocontrol fungus.

Genes and gene patterns involved in the cross-talk between Trichoderma mycoparasites and fungal hosts before the physical contact (sensing phase).

The development of biocontrol agents for agricultural purposes is based on the understanding of biological principles of their action. Nowadays, genome-wide studies are contributing to unravel genomic traits underpinning mycoparasitic behaviours. Nonetheless, genome mining approaches are helping in discovering such traits responsible for non-target effects and non- specific toxic secondary metabolites production (Kim and Vujanovic, 2016).

Cell Wall Degrading Enzymes (CWDEs) play a significant role in the sensing phase of Trichoderma interactions. In 2009, Seidl and colleagues published one of the first omics study on Trichoderma spp. as mycoparasite. The analysis of Expressed Sequence Tags (ESTs), aimed at a large-scale identification of genes required for the mycoparasitic interaction in Trichoderma atroviride, has revealed that most of the overexpressed ESTs were involved in post-translational processing and amino acid metabolism, suggesting an adaptation to environmental stresses. In addition, amino sugar catabolism was enhanced in mycoparasitic conditions, which is along with the first contact-independent stage of the mycoparasitic interaction (e.g., overexpression of CWDEs).

Different mycoparasitic behaviours depend on gene regulations at the early recognition stages in Trichoderma. Atanasova and colleagues (2013) have analysed the transcriptional responses

of customized tiling arrays. These mycoparasites were chosen on the basis of their different mycoparasitic behaviours. In fact, T. virens and T. atroviride have been considered as predator (immediate kill and consumption) and parasite, respectively; whereas T. reesei as a weak mycoparasite, according to the loss of mycoparasitic potential due to recent adaptation to certain ecological niches. In fact, it is believed that T. reesei has become a saprotroph on dead wood in consequence of parasitizing wood-degrading fungi in their habitat (Druzhinina et al., 2011). Interestingly, the most significant differences in transcriptional responses have been found at the pre-contact stage. Overall, T. virens and T. atroviride expressed a number of genes related to antagonism, while T. reesei expressed genes for a fast nutrient acquisition.

Concerning the most aggressive mycoparasites, T. virens predates R. solani by means of the production of gliotoxin, which inhibits protein and nucleic acid synthesis, whereas T. atroviride parasitizes the fungal prey by combining two strategies such as antibiosis and the production of hydrolytic enzymes (ß-glucanases, GH-16).

The ability to sense the fungal prey is believed to be a common property among Trichoderma mycoparasitic species (Seidl et al., 2009), often leading to the expression of hydrolytic enzymes such as contact-independent Cell Wall Degrading Enzymes (CWDEs). In this context, the first characterized CWDEs genes have been prb1 (Cortés et al., 1998) and ech42 (Zeilinger et al., 1999).

Mycoparasitic Trichoderma isolates can be effective against a wide range of fungal pathogens thanks to the recognition of conserved domains on host cell wall. In 2011, Reithner and colleagues have performed one of the first NGS-based high-throughput transcriptome-wide sequencing (GS20-454 sequencing) aimed to obtain an overview of mycoparasitic related processes in T. atroviride IMI206040. Interestingly, genes involved in metabolism were upregulated during mycoparasitic interactions (“interaction zone”, i.e. after contact) with R.

solani. Authors have proposed that degradation of host fungus cellular structures, as a consequence of the action of contact-independent hydrolytic enzymes, increases the availability of nutrients, thus triggering T. atroviride in its growth and fitness. Remarkably, the transcription of swo1, which shares similarity to plant expansins, was upregulated before contact with R. solani, Botrytis cinerea and Phytophthora capsici; whereas a homologous gene of T. reesei axe1, an acetyl xylan esterase, was just strongly induced in presence of P. capsici (oomycete). Both swo1 and axe1 harbour a cellulose binding domain (CBD), which can also

of gene containing CBD domains, such as swo1 and axe1, during the early stage of mycoparasitic interactions in Trichoderma species.

Genes have multiple functions depending on interactions. Recently, the antagonistic role of a metalloprotease nmp1 has been defined in Trichoderma guizhouense NJAU 4742. T.

guizhouense NJAU 4742 is a promising biocontrol agent against banana wilt disease (Panama disease) caused by Fusarium oxysporum f. sp. cubense 4 (Zhang et al., 2015). An up-regulation of nmp1 transcripts has been detected in T. guizhouense NJAU 4742 during the sensing phase (i.e., before contact) in all tested dual cultures with selected fungal pathogens. Interestingly, the expression of nmp1 was also high at the sensing phase of the T. guizhouense NJAU 4742 self-interaction, while it was growing on dead fungal biomass as the only carbon source. This latter result was not confirmed neither when T. guizhouense NJAU 4742 grew alone on dead fungal biomass nor on PDA in confrontation with itself. All these data suggested the involvement of nmp1 in mycotrophic interactions on dead fungal biomass. Noteworthy, the mycotrophic interaction is also triggered by the presence of the fungus itself. Authors have proposed that nmp1 may be involved in the sensing phase and also in the necrotrophic stage as well as for the degradation of its own hyphae.

Members of mycoparasitism-related gene families may have compensative effects. Chitinases (Glycoside Hydrolase 18, GH-18) produced by mycoparasites affect host fungus by weakening the cell-wall structure and facilitating the diffusion of toxic metabolites. The chitinase gene family is particularly expanded in Trichoderma spp. suggesting an important role of these enzymes in the adaptation to ecological niches (Karlsson and Stenlid 2008; Ihrmark et al. 2010;

Tzelepis et al., 2014). Baek and colleagues (1999) have described the ecological function of cht42, an endochitinase from a gliotoxin-producing strain of Trichoderma virens. The cht42 gene shows high similarity with Trichoderma harzianum ech-42, one of the most abundantly expressed secreted chitinase (Carsolio et al. 1994). T. harzianum ech-42 is expressed during mycoparasitic interactions (Lorito et al., 1996) and it is also induced in chitin-supplemented medium (Carsolio et al. 1994). The T. harzianum ∆cht42 deletion mutant has had a decreased biocontrol activity against R. solani. Also, T. harzianum ∆cht42 deletion mutant induced increased transcription of chit4 and chit5 genes compared with the wild type strain when they were cultured in a medium supplemented with chitin. Authors have supposed that the increased expression of chit4 and chit5 genes likely compensated for the loss of the endochitinase from

sometimes overlapping functions, including the release of nutrients from chitinous substrates (Peberdy 1990).

Conserved single-copy genes play fundamental roles in mycoparasitism. Two independent studies have pointed out the role for the chitin-induced fungal cell wall protein qid3, previously characterized in T. harzianum (Lora et al. 1994; Lora et al., 1995), both in cell wall protection and adhesion to hydrophobic surfaces, a function similar to hydrophobins (Vizcaíno et al. 2007;

Rosado et al., 2007). The single copy qid3 gene is present in each sequenced Trichoderma available to date, suggesting an important conserved function in this genus. These genes are rich in cysteine residues with no conserved domains identified. The exact role of qid3 is still unknown but it has been proposed that it is a cell wall bound protein essential for cell–cell attachment. Thus, qid3 may be functioning in host recognition and attachment (Lora et al.

1995).

Transcriptome analyses involving Fusarium-fungal interactions.

Literature is lacking of genome-wide transcriptomic studies on fungal-fungal interactions involving Fusarium species. Most of the genome-wide transcriptomic studies involving Fusarium species have been focused on fungal-plant interactions and, in particular, gene patterns have been evaluated from the host point of view. As instances, Fusarium oxysporum f. sp. cubense tropical race 4 induced the expression of genes related to phenylalanine and linolenic acid metabolism as well as the phenylpropanoid biosynthesis in banana plants (Wang et al., 2012). Quantitative trait loci linked to F. graminearum resistance of two near-isogenic lines of wheat have been investigated during infection. Genes contributing to the Fusarium resistance in wheat coded for an extensin-like cell wall protein, a wax synthase, three heat shock proteins 20, a lipid-transfer protein and an MDR-like ABC transporter (Schweiger et al., 2013). Recently, the transcriptomic profile of chickpea in presence of Fusarium toxins has been elucidated in order to develop wilt resistance varieties (Taxak et al., 2017). A transcriptomic study has involved Ustilago maydis and Fusarium verticillioides (both able to infect maize) grown, together or alone, for seven days in a liquid medium. The production of mycotoxins and cell wall degrading enzymes by F. verticillioides led to a decreased biomass of U. maydis. On the other hand, U. maydis counteracted by up-regulating genes involved in iron competition (siderophores) and, also, lineage specific genes. Authors proposed that the

Trichoderma and Fusarium: two hypocrealean cosmopolitan genera.

Trichoderma as generalist necrotrophic mycoparasite. Within the Hypocreales order, Trichoderma spp. are among the most exploited biocontrol agents. Trichoderma spp. have been developed as commercially available biocontrol agents (BCAs) thanks to their beneficial abilities such as antibiosis, mycoparasitism, competition for nutrients or ecological niches, induced systemic resistance in plants against plant pathogens (a recent comprehensive overview of modes of action exploited by BCAs are given in Kim and Vujanovic, 2016;

interested readers can also read Baker and Cook, 1974 and Wei et al. 1996). Furthermore, together with Clonostachys rosea (Karlsson et al., 2015), Trichoderma spp. are the most studied mycoparasites within the Hypocreales order. Recently, mycoparasitism has been defined as <<a parasitic fungus-fungus relationship in which the mycoparasite interacts with the host cell to perform its biological cycle>> (Kim and Vujanovic, 2016). The complexity of mycoparasitic interactions has been described as follows: << [..] in other words, a

"mycoparasitic lifestyle" translates into a "mycoparasitic behaviour". Thus, the latter can be defined as a fungus receptive phenomenon, interactive with the surrounding environment, and recognizable by the alteration in the mycoparasite’s phenotypical and physiological traits in response to particular environmental condition(s) when meeting its fungal hosts.>> (Kim and Vujanovic, 2016). This complexity has already arisen in ‘60s, thanks to the pioneering works of Barnett (1963) and Boosalis (1964) aimed to describe biotrophic and necrotrophic mycoparasitic interactions.

The lifestyle of the Trichoderma genus has been redefined mycotrophic rather than mycoparasitic because Trichoderma spp. can also feed on dead fungal biomass (Druzhinina et al., 2011). Anyway, the first step of a mycotrophic or mycoparasitic interaction is the chemotropic growth towards the fungal prey during the sensing phase. Once the mycoparasite physically reaches the host fungus, it attaches to the surface of prey hyphae forming short loops and coilings around them and eventually penetrating the cell wall. At the end of this process, the mycoparasite feeds on prey’s cell content. As pointed out above, the molecular cross-talk starts before the physical contact between fungi. Host sensing and signalling lead to transcriptional reprogramming and to the secretion of effectors, enzymes, and secondary metabolites (Karlsson et al., 2016).

In summary, generalist necrotrophic mycoparasites, such as Trichoderma spp., benefit at the expense of the prey fungus while sharing the same ecological niche. Thus, depending on these

variables, members of the Trichoderma genus are able to show mycoparasitic traits (characterizing a mycoparasitic behaviour) during their lifecycle.

Phytopathogenic fusaria. The Fusarium genus gathers many plant pathogens of agricultural importance. Some of these species threaten animal and human health by producing mycotoxins during infections. As instance, F. graminearum, one of the main causal agent of Fusarium Head Blight (FHB) affecting cereals worldwide, infects wheat especially during anthesis. In addition to the damaged kernels, F. graminearum produces mycotoxins on them.

Fusarium is able to infect plants both in agricultural and natural environments causing rots, blights, wilts and cankers (Ma et al., 2013). Nowadays, the Fusarium genus includes almost three hundred species (Geiser et al., 2013). Despite phytopathogenic fusaria are generally classified as necrotrophs (killing and consumption of host cells) sometimes they also act as hemibiotrophic pathogen at the very early stages of infection as in the case of F. oxysporum wilts. Phytopathogenic fusaria may penetrate from roots (soilborne pathogens) or through aboveground parts of host plants (water and/or air dispersed).

Phytopathogenic fusaria may have a broad range of host specificity, as in the case of F.

verticillioides, or they may be extremely specialized on single host plants, as in the case of F.

oxysporum formae specialis (e.g., F. oxysporum f. sp. lycopersici affecting tomato).

An overview of Trichoderma gamsii and Fusarium graminearum species.

Trichoderma gamsii sp.: updated literature of this emerging species. T. gamsii is being developed and applied in diverse fields of science. An isolate of T. gamsii has been already commercialized as effective biopesticide. Remedier® is a preventive biopesticide developed by ISAGRO Spa based on two beneficial Trichoderma isolates, Trichoderma asperellum ICC012 and T. gamsii ICC080. In particular, T. gamsii ICC080 was isolated from Sardinian fields (Italy). Remarkably, in certain areas of this Italian region T. gamsii was the exclusive Trichoderma species to be isolated (Migheli et al., 2009). Both organisms control soil-borne pathogens infecting vegetable and ornamental crops. Target pathogens belong to widespread species such as Pythium spp., Phytophthora spp., Sclerotium spp., Rhizoctonia spp., Thielaviopsis basicola, Sclerotinia spp., Verticillium spp. Other T. gamsii isolates are being developed as BCA. An endophytic isolate of T. gamsii has shown antagonistic abilities by

T. gamsii (NFCCI 2177) has been isolated from the roots of lentil in a mountain ecosystem. T.

gamsii NFCCI 2177 has been able to solubilize tricalcium phosphate (TCP) in lab assays.

Furthermore, T. gamsii NFCCI 2177 has showed antagonistic traits through the production of both diffusible and volatile compounds against a wide range of phytopathogenic fungi (Rinu et al., 2014). Noteworthy, T. gamsii could be also applied in bioremediation. In fact, T. gamsii FCR16 is effective in removing hexavalent chromium ions from acidic electroplating effluent contaminated with high level of these and other coexisting metal ions (Kavita and Keharia, 2012). T. gamsii could also be applied in cancer medicine as cytochalasans producer.

Cytochalasans are bio-functional alkaloids displaying a wide range of biological features, such as anticancer, antimicrobial and antiparasitic activities. Cytochalasans were also found to possess phytotoxic activities. Scherlach and colleagues (2010) have also suggested cytochalasans producers may gain an ecological advantage by deterring competitors, enhancing their fitness to access nutrition, food and living space. A ‘talented strain’ of T.

gamsii, isolated from Panax notoginseng, is able to synthesize a range of cytochalasans, which take place from the PKS-NRPS hybrid pathway (Ding et al., 2015). T. gamsii could have effective application also in clinical microbiology. T. gamsii IPT853, isolated from sugar cane plantation soil, has shown the biogenic capacity of producing silver nanoparticles (AgNPs).

These extracellular AgNPs are active against Streptococcus aureus, Escherichia coli and Pseudomonas aeruginosa. (Ottoni et al., 2017).

Fusarium graminearum is one of the main causal agent of the Fusarium Head Blight (FHB) disease. Fusarium Head Blight (FHB) is a destructive disease affecting cereals worldwide. In 2014, harvested area and production of wheat accounted for 220 million hectares and 729 million tonnes worldwide, respectively (FAOSTAT). Fusarium graminearum and Fusarium culmorum are the prevalent causal agents of FHB (Parry et al., 1995), albeit more than 15 species contribute to this disease (Aoki et al., 2014).

F. graminearum has been considered a single cosmopolitan species for many years, as morphological identification was not able to resolve alone phylogenetic lineages. To this end, O’Donnell and colleagues (2000) have identified seven distinct phylogenetic lineages forming the F. graminearum species complex, which arose as a consequence of geographic and host isolation. A following biogeographic hypothesis has suggested that the species complex was born in the southern hemisphere and thereafter evolved in the northern one (Starkey et al., 2007). Recently, the number of species within the complex has increased up to fourteen, with

concern, all the species inside the F. graminearum species complex are able to produce B trichothecenes on host plants (O’Donnell et al., 2008). Moreover, the four different trichothecenes chemotypes identified in the F. graminearum species complex are not lineage specific, making the integrated disease control challenging.

On the biological background of T. gamsii T6085 and F. graminearum ITEM 124.

Biological control strategies against FHB developed by the Plant Pathology & Mycology laboratory (University of Pisa, Italy). To date, different control strategies may be effective in controlling Fusarium Head Blight (FHB), but none of which is effective alone. These practices include adequate crop rotations and usage of a combination of fungicides and tillage practices aimed to the management of crop residues (Wegulo et al., 2011). In this scenario, integrated pest management involving the development of biocontrol agents able to control FHB is a promising perspective (Matarese et al., 2012). During the disease cycle, crop residues and spikes at anthesis are considered as the most critical points: crop debris are used by pathogens to overwinter and produce ascospores that will become the initial inoculum at anthesis that represents the most susceptible phase for fungal infection. Within a defence program for a biological management of FHB, application of beneficial organisms, such as antagonistic fungi, during these two steps could be a successful approach to reduce the disease incidence and to prevent the risk of mycotoxin accumulation.

Tgam has been isolated from uncultivated soil in Crimea (Ukraine, EU). It has shown to tolerate a high concentration of deoxynivalenol (DON, 50 ppm) in in vitro condition (the limit of DON accumulation on durum wheat in Europe is 1.75 ppm). Tgam is able to antagonize F.

graminearum, one of the main causal agent of Fusarium Head Blight (FHB) on wheat, ITEM 124 (Fgra) by reducing its growth in dual confrontation assays and parasitizing its hyphae by forming short loops and coilings (mycoparasitic traits).

short loops (red arrows)

Fusarium

graminearum T6085

T6085

Fusarium graminearum

400X

T. gamsii T6085 (Tgam) has been successfully applied in field on wheat crops at the anthesis phase during two following growing seasons. This application has led to a reduction of both incidence and severity of FHB disease (Matarese, 2010; Matarese et al., 2012; Sarrocco et al., 2013). In 2016, the draft genome of Tgam has been publicly released, consisting of 38 Mbp and 10,944 predicted protein-coding genes (Baroncelli et al., 2016). A first comparative genomic analysis comprising Trichoderma spp. and other fungal model organisms has highlighted expansions in gene families related to mycoparasitism, such as glutamic and serine peptidases and specific carbohydrate-degrading enzymes (Baroncelli et al., 2014). In order to improve the effects of this beneficial Trichoderma isolate, a multitrophic approach has been evaluated by testing Tgam in combination with a Fusarium oxysporum isolate (F. oxysporum 7121). F. oxysporum is a well-known competitor for cultural debris of the main causal agents of FHB and particularly this strain was isolated from wheat straw pieces sown in a soil with a previous long history of cultivation to wheat (Sarrocco et al., 2012). Despite a large number of formae speciales, able to cause wilt in many plant species, no information about a f. sp. able to attack wheat are available. In addition, preliminary tests performed by using F. oxysporm 7121 and Tgam together, showed that nor Tgam neither F. oxysporum 7121 are affected and affect each other. F. oxysporum 7121 is also able to grow in presence of high concentration of DON (50 ppm), a prerequisite for acting as antagonist of DON-producer fungi (Sarrocco et al., 2012).

Finally, recent experiments showed that Tgam and F. oxysporum 7121 can significantly reduce the ability of Fgra to colonize rice and wheat kernels – also by reducing DON and its acetylated derivate production – and can cause a significant reduction in perithecia development by the pathogen, opening the possibility to further investigate the combined use of these two isolates on wheat (Sarrocco et al., Phytyopathology, submitted).

2.0 Materials and methods

2.1 Genome sequencing projects 2.1.1 Tgam v1.0

Tgam was cultured on Potato Dextrose Agar (PDA; Difco) containing a sterilized cellophane filter laid onto medium’s surface to facilitate fungal biomass harvesting. Inoculated plates were maintained at 25°C, 12 h light/12 h darkness, until fungal biomass was enough for DNA extraction protocol. The genomic DNA of Tgam was extracted using the DNeasy® plant mini kit (Qiagen) according to the manufacturer’s instructions. Genome sequencing services were carried out at the McGill University and Génome Québec Innovation Centre (Montréal, Québec, Canada) and they consisted of: i) high-throughput QC for sequencing libraries; ii) TruSeq® DNA library preparation and iii) Illumina MiSeq – Paired-ends 250bp sequencing run. Reads of 250bp were assembled using Velvet v1.2.08 (Zerbino and Birney, 2008). The completeness of the assembly was assessed using CEGMA v2.4 (Parra et al., 2007). The nuclear genome was annotated using the MAKER2 pipeline (Cantarel et al., 2008). The proteome of T. gamsii T6085 was scanned by WolfPSORT (Zerbino and Birney, 2008) for identifying secreted proteins.

2.1.2 Tgam v2.0

Reads from the genome project v1.0 were assembled using SPAdes v3.8.2 (Bankevich et al., 2012). The completeness of the assembly was assessed using BUSCO v12 (Simão et al., 2015).

The nuclear genome was annotated using the MAKER2 pipeline (Cantarel et al., 2008), which was implemented by reads from RNA-sequencing. At first, raw reads were assembled by SPAdes v3.8.2 (Bankevich et al., 2012). Then, the assembled transcripts were used to improve the gene prediction as the following Figure 1 summarises.

Figure 1. Gene prediction workflow using MAKER2 pipeline.

The proteome of Tgam was scanned by SignalP v4.1 (Petersen et al., 2011) for identifying secreted proteins.

2.1.3 Fgra v1.0

A spore suspension of Fgra was recovered from a 10-day-old culture and inoculated in a 500 mL flask filled with 125 mL of Yeast Peptone Dextrose (YPD, Sigma-Aldrich). The flasks were then incubated at 150 rpm, in the dark, at a temperature of 25°C. After two days, the mycelium was harvested by filtration using a sterile sheet of Miracloth (Calbiochem, San Diego, CA). The harvested mycelium was washed with sterile water, dried and stored at -20°C until use. Prior DNA extraction, a homogenization step using the bead-beating method through a BeadBug™ Microtube Homogenizer (Benchmark Scientific Inc., NJ) was performed. Total genomic DNA was extracted using the Quick-DNA™ Fungal/Bacterial Miniprep Kit (Zymo Research), following manufacturer’s instructions. Genome sequencing services were carried out at the BMR Genomics (Padova, Italy) and they consisted of: (i) High Throughput QC for sequencing libraries; (ii) KAPAHyperPlus gDNA library preparation and (iii) Illumina MiSeq – Paired-ends 300bp sequencing run following manufacturer’s instructions (Zapparata et al., 2017). Reads were assembled using SPAdes v3.8.2 (Bankevich et al., 2012). The completeness of the assembly was assessed using BUSCO v12 (Simão et al., 2015). The nuclear genome was annotated using the MAKER2 pipeline (Cantarel et al., 2008), which was implemented by reads from RNA-sequencing. At first, raw reads were assembled by SPAdes v3.8.2 (Bankevich et al., 2012). Then, the assembled transcripts were used to improve the gene prediction as the above-mentioned scheme summarises. The proteome of Fgra was scanned by SignalP v4.1

AUGUSTUS

GENEMARK ab initio gene prediction gene prediction model Transcripts

Multispecies proteins (Swiss-Prot) Genome sequence

BUSCO

Gene structure core-genes

MAKER2

2.2 Comparative genomic analyses

2.2.1 Genome sampling in the Hypocreales order

Twenty-five fungal organisms belonging to the Hypocreales order were selected for comparative genomic analysis. The fungal pathogen Magnaporthe oryzeae (Magnaporthales, Magnaporthaceae) was selected as outgroup. The rationale of the selection followed these criteria: 1) at least one organism per family (if gene prediction was available), 2) organisms having better genome assemblies were preferred, 3) all the available Trichoderma spp. and Fusarium spp. organisms having public gene prediction were selected (choice made in July 2017). OrthoFinder v0.4 (Emms and Kelly, 2015) was used to identify orthogroups among selected organisms. Following, the software MIRLO (https://github.com/mthon/mirlo) identified the most informative (‘phylogenetic signal’) single-copy orthogroups. On the basis of the top ten most informative loci, MrBayes 3.2.1 (Ronquist et al., 2012) was used to generate a phylogenetic tree.

2.2.2 Identification of expanded/contracted InterPro IDs

The genome sequences of the Hypocreales members were annotated by InterPro identifiers (IPR IDs) using RunIprScan 1.1.0 (http://michaelrthon.com/runiprscan). Once IPR IDs were collected, their expansions and contractions were examined both at species (Tgam and Fgra) and genus (Trichoderma spp. and Fusarium spp.) level. In particular, we treated F.

graminearum ITEM 124 and F. graminearum PH-1 as a single species. We used the following criterion to establish expansions at species level: if a given IPR ID count was higher than the maximum IPR ID count among all the other organisms, than that IPR ID was considered expanded. Likewise, if a given IPR ID count was lower than the minimum IPR ID count among all the other organisms, than that IPR ID was considered contracted. The same approach was also valid when expansions/contractions were analysed at genus level. In particular, the IPR ID maximum and minimum counts, to be compared to those of the other organisms, were selected within the genus itself.

2.3 Biological assays of the sensing phase 2.3.1 Experimental set up.

Sterilized nylon discs (Durapore® Membrane Filters; pore size 0.45µm; HVLP00010) were

Italiana). Then, PDA strips (45mm x 4mm) colonized by Tgam and Fgra were transferred on nylon discs seven centimetres apart each other (Figure 2):

Figure 2 Experimental set up.

Full black rectangles represent colonized strips. Distance between colonized strips is represented by a dashed line.

The experimental theses were: (i) Tgam self-interaction (Tgam vs Tgam), (ii) Fgra self- interaction (Fgra vs Fgra) and (iii) non-self-interaction (Tgam vs Fgra). Each thesis consisted of six and three independent biological replicates for pilot experiment and RNA sequencing project, respectively. Inoculated plates were placed at 25°C, 12 h light/ 12 h darkness, until the ending point of the experiment was reached, i.e. when edges of the colonies were eight to five millimetres apart from one another. The following Figure 3 reports on a non-self-interaction at the time of mycelia harvesting.

Figure 3 Example of a fungal-fungal interaction at the time of mycelium harvesting.

The edge of the colony was harvested and shock frozen in liquid nitrogen prior RNA extraction. On the left side, Tgam; on the right side, Fgra.

The fungal biomass was harvested from the edges of the colonies using a pre-chilled spatula and immediately shock frozen in liquid nitrogen. Frozen mycelium was used for RNA

2.3.2 pH measurement.

Following the experimental set-up described in section 1.1.1, each experimental thesis (Tgam self-interaction, Fgra self-interaction, non-self-interaction, Tgam alone, Fgra alone and uninoculated plates) was placed at 25°C, 12 h light/ 12 h darkness until colony’s edges were eight to five millimetres apart from one another (fungal-fungal interactions). The pH was measured between the colonies’ edges through a pH tester HI 98128 (HANNA instruments).

As regard to Tgam alone, Fgra alone and uninoculated plates, the pH was measured at the same time of fungal-fungal interactions and within the same distance from the colony edge (5-8 mm).

Each thesis consisted of three biological replicates and each biological replicate consisted of three technical replicates. pH measurements were statistically evaluated by analysis of variance (one-way ANOVA) and pairwise comparisons of means were made by Tukey–Kramer test (P<0.05).

2.3.3 Growth rate measurement in dual plate confrontation assay.

Following the experimental set-up described in section 1.1.1, each experimental thesis (Tgam self-interaction, Fgra self-interaction, non-self-interaction, Tgam alone and Fgra alone) was placed at 25°C, 12 h light/ 12 h darkness. Measurements were taken until colony’s edges were eight to five millimetres apart from one another (fungal-fungal interactions). Each thesis consisted of four biological replicates. Each biological replicate consisted of three technical measurements (that is the distance between the inoculum stripe – at the left/right borders and at the centre - and the colony edge). Measurements were taken at 7, 22, 27, 32, 47 and 50 hours after inoculation. Measurement values were used to create growth curves and the linear phase was subjected to an analysis of variance of regression to compare slopes (significance level, P<0.01).

2.4 Genome-wide transcriptomic analysis of the sensing phase between Tgam and Fgra.

2.4.1 Pilot experiment.

Two different RNA extraction protocols were compared in order to choose the best one for RNA sequencing. Please refer to the following section ‘experimental set up’ for an in-depth analysis of the experimental set up. In particular, we compared the TRIzol™ Reagent (Thermo Fisher Scientific) and the RNeasy® Plant Mini Kit (Qiagen) extraction protocols, following

RNeasy® MinElute™ Cleanup Kit (Qiagen), following the manufacturer’s instructions. RNA integrity was checked by electrophoresis: TAE (Tris-acetate-EDTA) 1X was used as running buffer (agarose 0.8%; voltage 60; 400 mA; running time 1.5 h) and 3 µL of each sample were loaded. RNA samples were quantified by Qubit™ fluorometer (Thermo Fisher Scientific) and quality checked by NanoDrop™ (Thermo Fisher Scientific) and 2100 Bioanalyzer (Agilent, chip RNA 6000 Pico), which outputs the RNA Integrity Number (RIN) and the 28S/18S ratio each sample.

2.4.2 RNA extraction and evaluation.

The fungal biomass was ground in liquid nitrogen using pre-chilled mortar and pestle. Total RNA was extracted using RNeasy® Plant Mini Kit (Qiagen) followed by DNase I treatment and RNeasy® MinElute™ Cleanup Kit (Qiagen), following the manufacturer’s instructions.

RNA integrity was checked by electrophoresis as above-mentioned (see section 2.0). Total RNA was quantified by Qubit fluorometer (Thermo Fisher Scientific) and its integrity was assessed using a 2100 Bioanalyzer (Agilent).

2.4.3 Library preparation and RNA sequencing.

Three out of six independent biological replicates were used for sequencing each experimental thesis. Libraries were prepared from 900 ng of total RNA using the TruSeq stranded mRNA sample preparation kit following the manufacturer’s recommendation. Libraries were quantified using Qubit fluorometer (Thermo Fisher Scientific), and their quality was checked by 2100 Bioanalyzer (Agilent Technologies). Time of fragmentation was modulated in order to obtain insert size between 120 and 210 bp. Libraries were sequenced at BMR Genomics (Padova, Italy) with an Illumina NextSeq500 sequencer, with 75bp paired-end reads. The raw reads were quality checked using the tool seqtk (https://github.com/lh3/seqtk) and FastQC v.0.11.5 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/).

2.4.4 Identification and annotation of differentially expressed genes (DEGs).

The software HISAT (v.2-2.0.0-beta) was used to align raw reads to the reference genomes.

Then, the software featureCounts (Liao et al., 2014), within the package subread v.1-5-3, was employed in order to count reads mapping to coding regions. On the basis of read counts, the function plotMDS (Ritchie et al., 2015) was used to plot log2 fold changes between the samples.

In Tgam, the self-interaction ‘C1’ separated apart from the other two biological replicates. For

differentially expressed genes (DEGs) between self- and non-self-interactions (Law et al., 2014; Liu et al., 2015). In summary, the contrast matrix was made in order to express the average log-expression of the non-self-interaction upon the self-interaction. Filtering threshold was set up to 0.5 count per million in at least 3 samples, in order to remove low count reads (Chen et al., 2016). After fitting a linear model to weighted data, results were summarized by the function topTable, which also performs hypothesis test and adjusts p-values for multiple testing (Phipson et al., 2016). The data were corrected for multiple testing by adjusting P- values to a false discovery rate (FDR) of 0.05. Following, the detailed R workflow employed:

>setwd("/path/of/the/working/directory/")

>raw.data <- read.table( file = "name.extension" , header = TRUE, sep = ",")

>counts <- raw.data[ , -c(1,ncol(raw.data)) ]

>rownames(counts) <- raw.data[ , 1 ]

>colnames(counts) <- paste(c(rep("C",3),rep("T",3)),c(1:3,1:3),sep="")

>colSums(counts) # Library Sizes

>colSums(counts) / 1e06 # Library Sizes in millions of reads

>group <- c(rep("C", 3) , rep("T", 3)) #DGEList() is the function that converts the count matrix into an edgeR object.

>cds <- DGEList (counts,group=group)

>cds <- cds[rowSums(1e+06 *

cds$counts/expandAsMatrix(cds$samples$lib.size, dim(cds)) > 0.5) >= 3, ] # low counts filtering

>dim (cds)

>cds <- calcNormFactors(cds, method="TMM" )

>vwts <- voomWithQualityWeights(cds, design=NULL, normalization="none", plot=TRUE) #Differential expression: Voom with sample quality weights

>vfit2 <- lmFit(vwts)

>vfit2 <- eBayes(vfit2)

>topTable(vfit2, coef=2, sort.by="P")

>top2 <- topTable(vfit2, coef=2, number=Inf,sort.by="P")

>sum(top2$adj.P.Val<0.05)

> summary(decideTests(vfit2))

The workflow was adopted following instruction in “edgeR Tutorial: Differential Expression in RNA-Seq Data” (https://cgrlucb.wikispaces.com/file/view/edgeR_Tutorial.pdf) and

“Limma: Linear Models for Microarray and RNA-Seq Data User’s Guide”

(https://www.bioconductor.org/packages/devel/bioc/vignettes/limma/inst/doc/usersguide.pdf) . On the basis of results obtained by Schurch and colleagues (2016), only DEGs having at least a log2 fold change of 1/-1 (that is a fold change value of at least 2/-2, respectively) were carried on for the following analysis. The annotation of DEGs was manually curated using Blastp (Altschul et al., 1990).

2.4.5 Analysis of secreted DEGs.

The proteomes of Fgra and Tgam were scanned by SignalP v4.1 (Petersen et al., 2011) for the identification of extracellularly secreted proteins. Thereafter, the secreted proteins differentially expressed during the non-self-interaction were further analysed.

2.5 Functional interpretation of DEGs

2.5.1 Analysis of expanded/contracted InterPro IDs involved in the sensing phase.

The DEGs of both Fgra and Tgam involved in the sensing phase were annotated by InterProScan (Jones et al., 2014). Thereafter, the expanded/contracted IPR IDs retrieved by comparative genomic analysis (section 2.2.2), both at species and genus level, were matched and further discussed.

2.5.2 GO term enrichment analysis on DEGs.

The genomes of Tgam and Fgra were annotated with GO terms retrieved by RunIprScan 1.1.0 (http://michaelrthon.com/runiprscan). The agriGO 2.0 platform (Tian et al., 2017) was used to perform a customized Singular Enrichment Analysis (SEA) aimed to the identification of enriched GO terms in the DEGs. Statistical analysis was performed by using the annotated reference proteomes as references, which were queried by up-/down-regulated genes of both fungal organisms. Enriched GO terms were detected using the Fisher’s exact test (multi-test adjustment method: Hochberg, FDR = 0.05). Note that the up-regulated proteins of Tgam were not analysed since less than ten entries were annotated with GO terms (default setting). Instead, a NCBI conserved domain analysis (Marchler-Bauer A et al., 2017) was performed on the up- regulated genes of Tgam.

2.5.3 GO term enrichment analysis on secreted DEGs.

The agriGO 2.0 platform (Tian et al., 2017) was used to perform a customized Singular Enrichment Analysis (SEA) aimed to the identification of enriched GO terms within DE secreted proteins. Statistical analysis was performed by using the annotated reference secretomes as references, which were queried by up-regulated secreted proteins of Fgra and down-regulated secreted proteins of Tgam. Enriched GO terms were detected using the Fisher’s exact test (multi-test adjustment method: Hochberg, FDR = 0.05). The down-regulated secreted proteins of Fgra and the up-regulated secreted proteins of Tgam were not analysed since less than ten entries were annotated with GO terms (default setting).

3.0 Results

3.1 Genome sequencing projects

3.1.1 Tgam v1.0

Mate-paired reads of 250 bp, accounting for a total of 3.80 Gbp (average coverage: 100X), were assembled in 381 scaffolds, with a maximum scaffold size of 1,198,811 bp. The total assembly length was 37.97 Mbp (N50=417,961). The GC content was 49.00%. The completeness of the assembly was estimated to be 97.58%. The nuclear genome annotation has resulted in 10,944 protein-coding genes. Of these, 1,356 proteins (12.39% of the proteome) were predicted to be secreted since they contain secretion signal peptide. This Whole-Genome Shotgun project has been deposited in GenBank under the accession N°: JPDN00000000 (BioProject PRJNA252048).

3.1.2 Tgam v2.0

Mate-paired reads from the v1.0 sequencing project were assembled in 172 scaffolds, with a maximum scaffold size of 1,830,400 bp. The total assembly length was 37.91 Mbp (N50=697,391). The GC content was 48.95%. The completeness of the assembly was estimated to be 99.7%. The nuclear genome annotation, using transcripts from RNA-seq as biological evidences, has resulted in 11,179 protein-coding genes. Of these, 1,046 predicted proteins (9.4% of the proteome) were predicted to be secreted since they contain secretion signal peptide. This assembly version is present in GenBank as GCA_001481775.2.

3.1.3 Fgra v1.0

Paired reads of 300 bp, accounting for a total of 3.16 Gbp (average coverage: 38X) were assembled in 67 scaffolds, with a maximum scaffold size of 7,305,194 bp. The total assembly length was 36.88 Mbp (N50=1,518,396). The GC content was 48.18%. The completeness of the assembly was estimated to be 99.86%. The nuclear genome annotation has resulted in 11,827 protein-coding genes. Of these, 1,288 predicted proteins (10.9% of the proteome) were predicted to be secreted since they contain secretion signal peptide. This Whole-Genome Shotgun project has been deposited in GenBank under the accession N°: NQOC00000000 (BioProject: PRJNA397367; BioSample: SAMN07455307).

3.2 Comparative genomic analyses

3.2.1 Genome sampling in the Hypocreales order

The following Table 1 reports on the selection of Hypocreales organisms with some useful information about strain code, lifestyle, genome assembly references and assembly level.

Table 1. Genome sampling in the Hypocreales order.

Assembly were downloaded by GenBank. Brackets in ‘assembly level’ column indicate the number of chromosomes/scaffolds.

family genus species strain

code brief lifestyle

highlight assembly assembly level

Hypocreaceae

Trichoderma gamsii T6085

Promising biocontrol agent of Fusarium Head

Blight (FHB)

GCA_001481775.

2

scaffolds (172)

Trichoderma atroviride ^IMI20604₀ Mycoparasite GCA_000171015.

2 scaffolds (29)

Trichoderma harzianum* T6776

promising beneficial biocontrol agent

GCA_000988865.

1

scaffolds (1572)

Trichoderma guizhouense ^{NJAU 4742} Mycoparasite GCA_002022785.

1 scaffolds (63)

Trichoderma reesei QM6a

Weak mycoparasite and

important to industry for its

cellulase production

GCA_000167675.

2

chromosomes (7)

Trichoderma virens Gv29-8 Mycoparasite GCA_000170995.

2 scaffolds (93)

Escovopsis weberi CC031208-10 A ceph

Mycoparasite of the crops of fungus-growing

ants

GCA_001278495.

1 scaffolds (29)

Nectriaceae

Fusarium avenaceum Fa05001 Plant pathogen GCA_000769215.

1 scaffolds (83)

Fusarium fujikuroi IMI5828

9 Plant pathogen GCA_900079805.

1

chromosomes (12)

Fusarium graminearum ^ITEM ₁₂₄ Mycotoxigenic

plant pathogen GCA_002352725.

1 scaffolds (67)

Fusarium graminearum PH-1 Mycotoxigenic

plant pathogen GCA_900044135.

1 chromosomes (6)

Fusarium langsethiae Fl20105 9

Mycotoxigenic plant pathogen

GCA_001292635.

1

scaffolds (1586)

family genus species strain code

brief lifestyle

highlight assembly assembly level Fusarium oxysporum f. sp.

lycopersici 4287 Plant pathogen GCA_000149955.

2

chromosomes (15)

Fusarium poae 2516 Plant pathogen GCA_001675295.

1 scaffolds

(181)

Fusarium proliferatum ET1

A mycotoxigenic fungus causing

disease in humans, animals

and plants

GCA_900067095.

1 scaffolds (32)

Fusarium pseudograminearu

m CS3096 Plant pathogen GCA_000303195.

2 chromosomes (4)

Fusarium verticillioides 7600 Mycotoxigenic plant pathogen

GCA_000149555.

1 scaffolds (37)

Neonectria ditissima R09/05 Plant pathogen GCA_001306435.

1

scaffolds (677)

Clavicipitaceae

Claviceps purpurea 20.1 Plant pathogen GCA_000347355.

1

scaffolds (191)

Metarhizium anisopliae ^{ARSEF 549} Biological control agent of parasitic

insects

GCA_000814975.

1 scaffolds (74)

Pochonia chlamydosporia 170 Biological control agent of nematodes

GCA_001653235.

1

scaffolds (114)

Cordycipitaceae Beauveria bassiana ^ARSEF ₂₈₆₀ Biological control agent of parasitic

insects

GCA_000280675.

1 scaffolds

(237)

Stachybotryaceae Stachybotrys chartarum IBT 7711

Mycotoxigenic fungus

GCA_000730325.

1

scaffolds (2290)

Ophiocordycipitace

ae Drechmeria coniospora ^ARSEF ₆₉₆₂ Endoparasitic of

nematodes GCA_001625195.

1

chromosomes (3)

**Hypocreales incertae sedis

Ustilaginoide

a virens UV-8b Plant pathogen GCA_000687475.

1

scaffolds (449)

Magnaporthales,

Magnaporthaceae Magnaporthe oryzae 70-15 Plant pathogen GCA_000002495.

2 chromosomes (7)

* recently reclassified as T. afroharzianum T6776; **likely belonging to Clavicipitaceae family (according to the phylogenetic tree in Figure 4)

On the basis of predicted genomes, OrthoFinder assigned 307265 genes (93.3% of total) to 16163 orthogroups. Of these, there were 3704 orthogroups with all species present and 1534 of these consisted of single-copy genes. The software MIRLO identified the top ten most informative single-copy orthogroups, which were (in order of ‘phylogenetic signal’):

(OG0005232) hypothetical protein; (OG0005439) separase; (OG0004020) related to myosin heavy chain; (OG0004078) related to inositol polyphosphate 5-phosphatase ocrl-1;

(OG0005474) superkiller protein 3; (OG0004307) DNA polymerase zeta subunit;

(OG0005569) related to SET3 complex; (OG0004928) acyl CoA ligase-like protein;

(OG0005383) related to papaya ringspot virus polyprotein; (OG0004771) U3 small nucleolar RNA-associated protein 4.

The phylogenetic tree in Figure 4 was built using MrBayes 3.2.1 by concatenating the above- mentioned loci.

Figure 4. Phylogenetic tree of Hypocreales based on the top ten most informative single-copy orthogroups made by MrBayes 3.2.1. SH-like support values are reported for each branch. Beauveria bassiana (BBAS), Claviceps purpurea (CPUR), Drechmeria coniospora (DCON), Escovopsis weberi (EWEB), Fusarium avenaceum (FAVE), Fusarium fujikuroi (FFUJ), Fusarium graminearum ITEM124 (FGRA124), Fusarium langsethiae (FLAN), Fusarium oxysporum (FOXY), Fusarium graminearum PH-1 (FGRAPH-1), Fusarium poae (FPOA), Fusarium proliferatum (FPRO), Fusarium pseudograminearum (FPSE), Fusarium verticillioides (FVER), Magnaporthe oryzae (MORY), Metarhizium anisopliae (MANI), Neonectria ditissima (NDIT), Pochonia chlamydosporia (PCHL), Stachybotrys chartarum (SCHA), Trichoderma atroviride (TATR), Trichoderma gamsii (TGAM), Trichoderma guizhouense (TGUI), Trichoderma harzianum (THAR), Trichoderma reesei (TREE), Trichoderma virens (TVIR), Ustilaginoidea virens (UVIR).

Magnaporthales, Magnaporthaceae MORY

NDIT FOXY

FVER FFUJ FPRO

FAVE FPOA FLAN FPSE FGRA124 FGRAPH1

Nectriaceae

Stachybotryaceae SCHA

Ophiocordycipitaceae DCON

MANI PCHL

UVIR CPUR

Clavicipitaceae

Cordycipitaceae BBAS

EWEB TATR

TGAM TREE TVIR

TGUI THAR

Hypocreaceae 100

100 100

94

100 100 100 38 100 100

100

100 100 100

100

100 100 100 100 100

87 100 100 0

0.10

3.2.2 Identification of expanded/contracted InterPro IDs

The comparative genomic analysis was based on the annotation of selected genomes through InterPro entries (IPR IDs), which univocally identify the domain topology of genes. We identified expanded/contracted InterPro IDs (IPR IDs) both at genus, Trichoderma/Fusarium, and species, Tgam/Fgra, level.

When the database of InterProScan (Jones et al., 2014) was queried by the genome sequences of the Hypocreales members, 6636 different IPR IDs were annotated. Escovopsis weberi and Fusarium oxysporum were the less and the more annotated genomes with 15119 and 40042 IPR IDs, respectively. This was mainly due to different genome sizes. On average, 24413 IPR IDs per genome were annotated. Looking at the standard deviation across annotated IPR IDs, the trend followed this rule of thumb: given an IPR ID, the larger its count among the genomes the highest standard deviation (e.g., IPR016040 NAD(P)-binding domain, standard deviation:

183.49). On the contrary, extremely conserved low count IPR IDs reached low standard deviation (e.g., IPR000023 Phosphofructokinase domain, standard deviation: 0.0).

The genomes of Fgra and Tgam were annotated with 24483 and 23184 IPR IDs spread out over 9061 (76.6%) and 8600 (76.9%) genes, respectively. The IPR ID mean count per gene was 2.7 for both. Overall, at species level, 32 and 31 IPR IDs were found expanded in F.

graminearum (ITEM 124 and PH-1) and Tgam, respectively; whereas 43 and 104 IPR IDs were found contracted in F. graminearum (ITEM 124 and PH-1) and Tgam, respectively. At genus level, 23 and 10 IPR IDs were found expanded in Fusarium and Trichoderma genera, respectively; whereas 0 and 1 IPR IDs were found contracted in Fusarium and Trichoderma genera, respectively. The Table 2 reports in detail the identified IPR IDs expanded and contracted both at species and genus level.

Table 2. Expanded and contracted IPR IDs.

These data were retrieved by querying the InterProScan database (March 2018). In detail, the “entry type” is represented by: (D) domain, (F) family, (H) homologous superfamily, (S) site, (R) repeat. “Overlapping homologous superfamilies”, where present, refer to homologous superfamily/ies related to that IPR ID. “GO terms”, where present, refer to assigned biological process, molecular function and/or cellular component.

IPR ID expansions - Trichoderma spp.

IPR ID Name entry type GO terms

IPR000111 Glycoside hydrolase family 27/36, S

GO:0005975 carbohydrate metabolic process; GO:0004553

IPR001110 Uncharacterized protein family UPF0012,

conserved site. S none

IPR007361 Domain of unknown function DUF427 D none

IPR008441 Capsular polysaccharide synthesis F none

IPR012939 Glycosyl hydrolase family 92 D none

IPR019843 DNA polymerase family X, binding site S GO:0016779 nucleotidyltransferase activity

IPR027414 Glycosyl hydrolase family, N-terminal

domain D none

IPR033130 Ribonuclease T2, His active site 2 S none

IPR033452 Glycosyl hydrolase family 30, beta

sandwich domain D none

IPR033453 Glycosyl hydrolase family 30, TIM-barrel

domain D

IPR ID expansions - Fusarium spp.

IPR ID Name entry type GO terms

IPR000782 FAS1 domain D none

IPR000863 Sulfotransferase domain D GO:0008146 sulfotransferase activity

IPR001063 Ribosomal protein L22/L17 F

GO:0006412 translation;

GO:0003735 structural constituent of ribosome; GO:0005840

ribosome

IPR001498 Impact, N-terminal D none

IPR002641 Patatin/Phospholipase A2-related D GO:0006629 lipid metabolic process

IPR004567 Type II pantothenate kinase F

GO:0015937 coenzyme A biosynthetic process; GO:0005524

ATP binding; GO:0004594 pantothenate kinase activity

IPR004695 Voltage-dependent anion channel F

GO:0055085 transmembrane transport; GO:0016021 integral

component of membrane

IPR005002 Phosphomannomutase F

GO:0009298 GDP-mannose biosynthetic process; GO:0004615

phosphomannomutase activity;

GO:0005737 cytoplasm

IPR005855 Glucosamine-fructose-6-phosphate

aminotransferase, isomerising F

GO:1901137 carbohydrate derivative biosynthetic process;

GO:0004360 glutamine-fructose-6- phosphate transaminase